Problem
Research questions and friction points this paper is trying to address.
Generative Marketing Mix Modeling
Causal Inference
Generative Engine Optimization
Generative Engine Marketing
Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
๐ค AI Summary
ๆฌๆๆๅบ็ๆๅผ่ฅ้็ปๅๆจกๅ(GMMM)ๆฅ่ฏไผฐ็ๆๅผๆไผๅ(GEO)ๅ็ๆๅผๆ่ฅ้(GEM)ๅฏนไผไธ็ๅฝฑๅ๏ผ้่ฟ็ปๅ็ๆ็ญๆกใ้ฎ้ข่ฎกๆฐๅๆณจๆๆฆ็็ญๆฐๆฎใ
๐ Abstract
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generative Marketing Mix Modeling
Causal Inference
Generative Engine Optimization
Generative Engine Marketing
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